% SUpDEq - Spatial Upsampling by Directional Equalization
function results = supdeq_testHPlocalization( testHRTFdataset, referenceHRTFdataset, samplingGrid, fs, printResults)
This function models horizontal plane localization for the testHRTFdataset
and the referenceHRTFdataset on a pre-defined or given sampling grid.
The function uses the GMM-based estimation of azimuth direction
according to May2011 (see references), which is part of the
Auditory Modeling Toolbox (AMT). The result is a struct with the azimuthal
errors in degree.
Output:
results - Output struct with errors and mean absolute
errors of the test/referenceHRTFdataset
Input:
testHRTFdataset - Struct with SH-coefficients of the test
HRTF dataset (for example the de-equalized HRTF set
or any other HRTF set based on the referenceHRTFdataset)
for the left (Hl_nm) and right (Hr_nm) channel/ear,
absolute frequency scale f, transform order N, and FFToversize
referenceHRTFdataset - Struct with SH-coefficients of the reference
HRTF dataset for the left (Hl_nm) and right (Hr_nm) channel/ear,
absolute frequency scale f, transform order N, and FFToversize
samplingGrid - Spatial sampling grid. Must be a Qx2 matrix where
the first column holds the azimuth and the second
the elevation.
If no samplingGrid (or []) is passed, the default
sampling grid is used with equidistant values
(steps of 5° in azimuth, elevation = 90° (sound source in the front),
azimuth range +-90°)
fs - Sampling Rate
Default: 48000
printResults - Print results true/false
Default:false
Dependencies: Auditory Modeling Toolbox (AMT)
References:
T. May, S. van de Par, and A. Kohlrausch. A probabilistic model for
robust localization based on a binaural auditory front-end. IEEE Trans
Audio Speech Lang Proc, 19:1-13, 2011.
(C) 2018 by JMA, Johannes M. Arend
TH Köln - University of Applied Sciences
Institute of Communications Engineering
Department of Acoustics and Audio Signal Processing0001 %% SUpDEq - Spatial Upsampling by Directional Equalization 0002 % 0003 % function results = supdeq_testHPlocalization( testHRTFdataset, referenceHRTFdataset, samplingGrid, fs, printResults) 0004 % 0005 % This function models horizontal plane localization for the testHRTFdataset 0006 % and the referenceHRTFdataset on a pre-defined or given sampling grid. 0007 % The function uses the GMM-based estimation of azimuth direction 0008 % according to May2011 (see references), which is part of the 0009 % Auditory Modeling Toolbox (AMT). The result is a struct with the azimuthal 0010 % errors in degree. 0011 % 0012 % Output: 0013 % results - Output struct with errors and mean absolute 0014 % errors of the test/referenceHRTFdataset 0015 % 0016 % Input: 0017 % testHRTFdataset - Struct with SH-coefficients of the test 0018 % HRTF dataset (for example the de-equalized HRTF set 0019 % or any other HRTF set based on the referenceHRTFdataset) 0020 % for the left (Hl_nm) and right (Hr_nm) channel/ear, 0021 % absolute frequency scale f, transform order N, and FFToversize 0022 % referenceHRTFdataset - Struct with SH-coefficients of the reference 0023 % HRTF dataset for the left (Hl_nm) and right (Hr_nm) channel/ear, 0024 % absolute frequency scale f, transform order N, and FFToversize 0025 % samplingGrid - Spatial sampling grid. Must be a Qx2 matrix where 0026 % the first column holds the azimuth and the second 0027 % the elevation. 0028 % If no samplingGrid (or []) is passed, the default 0029 % sampling grid is used with equidistant values 0030 % (steps of 5° in azimuth, elevation = 90° (sound source in the front), 0031 % azimuth range +-90°) 0032 % fs - Sampling Rate 0033 % Default: 48000 0034 % printResults - Print results true/false 0035 % Default:false 0036 % 0037 % Dependencies: Auditory Modeling Toolbox (AMT) 0038 % 0039 % References: 0040 % T. May, S. van de Par, and A. Kohlrausch. A probabilistic model for 0041 % robust localization based on a binaural auditory front-end. IEEE Trans 0042 % Audio Speech Lang Proc, 19:1-13, 2011. 0043 % 0044 % (C) 2018 by JMA, Johannes M. Arend 0045 % TH Köln - University of Applied Sciences 0046 % Institute of Communications Engineering 0047 % Department of Acoustics and Audio Signal Processing 0048 0049 function results = supdeq_testHPlocalization( testHRTFdataset, referenceHRTFdataset, samplingGrid, fs, printResults) 0050 0051 %Sampling grid on horizontal plane with +-90 degree in steps of 5 degree 0052 if nargin < 3 || isempty(samplingGrid) 0053 [~, samplingGrid] = AKsubGrid(5, 'transverse', 0); 0054 %Transform elevation to required range 0055 samplingGrid(:,2) = 90-samplingGrid(:,2); 0056 %Set az to range between -90 and 90 degree 0057 ids = [find(samplingGrid(:,1)<=90);find(samplingGrid(:,1)>=270)]; 0058 samplingGrid = samplingGrid(ids,:); 0059 end 0060 0061 if nargin < 4 || isempty(fs) 0062 fs = 48000; 0063 end 0064 0065 if nargin < 5 0066 printResults = false; 0067 end 0068 0069 %% Run model may2011 0070 0071 %Generate noise with a length of 1 second 0072 noise = randn(fs,1); 0073 %Set to -6dB peak level 0074 noise = noise*((10^(-6/20))/(max(abs(noise)))); 0075 0076 % Get HRIRs according to sampling grid 0077 fprintf('Extracting 2 x %d HRIRs. This may take some time...\n',size(samplingGrid,1)); 0078 [HRIRs_Test_L,HRIRs_Test_R] = supdeq_getArbHRIR(testHRTFdataset,samplingGrid,[],[],'ak'); 0079 [HRIRs_Ref_L,HRIRs_Ref_R] = supdeq_getArbHRIR(referenceHRTFdataset,samplingGrid,[],[],'ak'); 0080 fprintf('2 x %d HRIRs extracted...\n',size(samplingGrid,1)) 0081 0082 disp('Running model according to May2011...'); 0083 az_Test = zeros(1,size(samplingGrid,1)); 0084 az_Ref = zeros(1,size(samplingGrid,1)); 0085 for i = 1:size(samplingGrid,1) 0086 0087 %Create binaural noise signal 0088 binNoise_Test(:,1) = conv(noise,HRIRs_Test_L(:,i)); 0089 binNoise_Test(:,2) = conv(noise,HRIRs_Test_R(:,i)); 0090 binNoise_Ref(:,1) = conv(noise,HRIRs_Ref_L(:,i)); 0091 binNoise_Ref(:,2) = conv(noise,HRIRs_Ref_R(:,i)); 0092 0093 %Test azimuthal position with may2011 0094 azStruct_Test = may2011(binNoise_Test,fs); 0095 azStruct_Ref = may2011(binNoise_Ref,fs); 0096 %Histogram analysis of frame-based localization estimates 0097 az_Test(:,i) = may2011_estAzimuthGMM(azStruct_Test,'HIST',1,0); 0098 az_Ref(:,i) = may2011_estAzimuthGMM(azStruct_Ref,'HIST',1,0); 0099 0100 if ~mod(i,5) 0101 fprintf('%d of %d azimuthal positions evaluated...\n',i,size(samplingGrid,1)) 0102 end 0103 0104 end 0105 0106 %% (2) - Prepare az values of samplingGrid for output format of may2011 0107 0108 azSamplingGrid = samplingGrid(:,1); 0109 azSamplingGrid(azSamplingGrid<=90) = -1*azSamplingGrid(azSamplingGrid<=90); 0110 azSamplingGrid(azSamplingGrid>90) = 360-azSamplingGrid(azSamplingGrid>90); 0111 0112 %% (3) - Calculate errors and write results struct 0113 0114 results.azError_Test = az_Test' - azSamplingGrid; 0115 results.azAbsError_Test = abs(results.azError_Test ); 0116 results.azError_Ref = az_Ref' - azSamplingGrid; 0117 results.azAbsError_Ref = abs(results.azError_Ref ); 0118 results.azMeanAbsError_Test = mean(results.azAbsError_Test); 0119 results.azMeanAbsError_Ref = mean(results.azAbsError_Ref); 0120 results.diff_azMeanAbsError = results.azMeanAbsError_Ref - results.azMeanAbsError_Test; 0121 results.azNonZeroMeanAbsError_Test = mean(nonzeros(results.azAbsError_Test)); 0122 results.azNonZeroMeanAbsError_Ref = mean(nonzeros(results.azAbsError_Ref)); 0123 results.diff_azNonZeroMeanAbsError = results.azNonZeroMeanAbsError_Ref - results.azNonZeroMeanAbsError_Test; 0124 results.azSamplingGrid = azSamplingGrid; 0125 results.samplingGrid = samplingGrid; 0126 0127 disp('Done with horizontal plane localization test...'); 0128 0129 %% (4) - Print results 0130 0131 if printResults 0132 fprintf('\nRESULTS \n'); 0133 disp('------------------------------------------------------'); 0134 fprintf('Mean absolute azimuthal error testHRTFdataset (deg) \t\t %4.1f \n',results.azMeanAbsError_Test) 0135 fprintf('Mean absolute azimuthal error referenceHRTFdataset (deg) \t %4.1f \n',results.azMeanAbsError_Ref) 0136 fprintf('Mean absolute azimuthal error difference (deg) \t\t\t %4.1f \n',results.diff_azMeanAbsError) 0137 disp('------------------------------------------------------'); 0138 fprintf('Non zero mean absolute azimuthal error testHRTFdataset (deg) \t %4.1f \n',results.azNonZeroMeanAbsError_Test) 0139 fprintf('Non zero mean absolute azimuthal error referenceHRTFdataset (deg) %4.1f \n',results.azNonZeroMeanAbsError_Ref) 0140 fprintf('Non zero mean absolute azimuthal error difference (deg) \t %4.1f \n',results.diff_azNonZeroMeanAbsError) 0141 disp('------------------------------------------------------'); 0142 end 0143 0144 end 0145